{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating the USD Liquidity Index with the OpenBB Platform\n",
    "\n",
    "This popular indicator is made from a simple subtraction of three FRED series that are published every Wednesday, and is often overlayed with risk assets like the S&P 500 Index or Bitcoin.  The OpenBB SDK is well suited for this task, let's take a look to create this index.\n",
    "\n",
    "The formula is defined as:\n",
    "\n",
    "```console\n",
    "WALCL (All Liabilities) – WLRRAL (RRP) – WDTGAL (TGA)\n",
    "```\n",
    "\n",
    "To get these data series, we will use the `openbb-fred` data extension and the `economy` module.  First thing is to import the Python interface, and we will also import Pandas to conduct some DataFrame operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openbb import obb\n",
    "from pandas import DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two `fred` functions in the `openbb-economy` router:\n",
    "\n",
    "- `obb.economy.fred_search()`\n",
    "- `obb.economy.fred_series()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OBBject\n",
       "\n",
       "id: 066c7874-2012-7189-8000-1e79898a8a3c\n",
       "results: [{'date': datetime.date(2002, 12, 18), 'WALCL': 719542.0, 'WLRRAL': 21905....\n",
       "provider: fred\n",
       "warnings: None\n",
       "chart: None\n",
       "extra: {'results_metadata': {'WALCL': {'title': 'Assets: Total Assets: Total Assets..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = obb.economy.fred_series([\"WALCL\", \"WLRRAL\", \"WDTGAL\", \"SP500\"])\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is metadata from each series in the warnings of the response object.  It can be recovered as a JSON dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['WALCL', 'WLRRAL', 'WDTGAL', 'SP500'])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Assets: Total Assets: Total Assets (Less Eliminations from Consolidation): Wednesday Level'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Millions of U.S. Dollars'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metadata = data.extra[\"results_metadata\"]\n",
    "\n",
    "display(metadata.keys())\n",
    "display(metadata[\"WALCL\"].get(\"title\"))\n",
    "display(metadata[\"WALCL\"].get(\"units\"))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying FRED\n",
    "\n",
    "If we didn't already know the ID for the series, we can search with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>series_id</th>\n",
       "      <th>title</th>\n",
       "      <th>observation_start</th>\n",
       "      <th>observation_end</th>\n",
       "      <th>frequency</th>\n",
       "      <th>frequency_short</th>\n",
       "      <th>units</th>\n",
       "      <th>units_short</th>\n",
       "      <th>seasonal_adjustment</th>\n",
       "      <th>seasonal_adjustment_short</th>\n",
       "      <th>last_updated</th>\n",
       "      <th>popularity</th>\n",
       "      <th>group_popularity</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WALCL</td>\n",
       "      <td>Assets: Total Assets: Total Assets (Less Elimi...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2024-08-14</td>\n",
       "      <td>Weekly, As of Wednesday</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2024-08-15 15:37:22-05:00</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>H41RESPPALDKNWW</td>\n",
       "      <td>Assets: Liquidity and Credit Facilities: Loans...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2024-08-14</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2024-08-15 15:37:01-05:00</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TREAST</td>\n",
       "      <td>Assets: Securities Held Outright: U.S. Treasur...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2024-08-14</td>\n",
       "      <td>Weekly, As of Wednesday</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2024-08-15 15:36:54-05:00</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>The total face value of U.S. Treasury securiti...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         series_id                                              title  \\\n",
       "0            WALCL  Assets: Total Assets: Total Assets (Less Elimi...   \n",
       "1  H41RESPPALDKNWW  Assets: Liquidity and Credit Facilities: Loans...   \n",
       "2           TREAST  Assets: Securities Held Outright: U.S. Treasur...   \n",
       "\n",
       "  observation_start observation_end                frequency frequency_short  \\\n",
       "0        2002-12-18      2024-08-14  Weekly, As of Wednesday               W   \n",
       "1        2002-12-18      2024-08-14                   Weekly               W   \n",
       "2        2002-12-18      2024-08-14  Weekly, As of Wednesday               W   \n",
       "\n",
       "                      units     units_short      seasonal_adjustment  \\\n",
       "0  Millions of U.S. Dollars  Mil. of U.S. $  Not Seasonally Adjusted   \n",
       "1  Millions of U.S. Dollars  Mil. of U.S. $  Not Seasonally Adjusted   \n",
       "2  Millions of U.S. Dollars  Mil. of U.S. $  Not Seasonally Adjusted   \n",
       "\n",
       "  seasonal_adjustment_short               last_updated  popularity  \\\n",
       "0                       NSA  2024-08-15 15:37:22-05:00          94   \n",
       "1                       NSA  2024-08-15 15:37:01-05:00          76   \n",
       "2                       NSA  2024-08-15 15:36:54-05:00          71   \n",
       "\n",
       "   group_popularity                                              notes  \n",
       "0                94                                                NaN  \n",
       "1                76                                                NaN  \n",
       "2                71  The total face value of U.S. Treasury securiti...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The first result is the series we are looking for as the starting value.\n",
    "\n",
    "obb.economy.fred_search(\"Wednesday Levels\").to_df().head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>series_id</th>\n",
       "      <th>title</th>\n",
       "      <th>observation_start</th>\n",
       "      <th>observation_end</th>\n",
       "      <th>frequency</th>\n",
       "      <th>frequency_short</th>\n",
       "      <th>units</th>\n",
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       "      <th>seasonal_adjustment_short</th>\n",
       "      <th>last_updated</th>\n",
       "      <th>notes</th>\n",
       "      <th>popularity</th>\n",
       "      <th>group_popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WLRRAL</td>\n",
       "      <td>Liabilities and Capital: Liabilities: Reverse ...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2024-08-14</td>\n",
       "      <td>Weekly, As of Wednesday</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2024-08-15 15:37:49-05:00</td>\n",
       "      <td>Reverse repurchase agreements are transactions...</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WLRRAFOIAL</td>\n",
       "      <td>Liabilities and Capital: Liabilities: Reverse ...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2024-08-14</td>\n",
       "      <td>Weekly, As of Wednesday</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2024-08-15 15:37:36-05:00</td>\n",
       "      <td>Reverse repurchase agreements are transactions...</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WLRRAOL</td>\n",
       "      <td>Liabilities and Capital: Liabilities: Reverse ...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2024-08-14</td>\n",
       "      <td>Weekly, As of Wednesday</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2024-08-15 15:37:40-05:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    series_id                                              title  \\\n",
       "0      WLRRAL  Liabilities and Capital: Liabilities: Reverse ...   \n",
       "1  WLRRAFOIAL  Liabilities and Capital: Liabilities: Reverse ...   \n",
       "2     WLRRAOL  Liabilities and Capital: Liabilities: Reverse ...   \n",
       "\n",
       "  observation_start observation_end                frequency frequency_short  \\\n",
       "0        2002-12-18      2024-08-14  Weekly, As of Wednesday               W   \n",
       "1        2002-12-18      2024-08-14  Weekly, As of Wednesday               W   \n",
       "2        2002-12-18      2024-08-14  Weekly, As of Wednesday               W   \n",
       "\n",
       "                      units     units_short      seasonal_adjustment  \\\n",
       "0  Millions of U.S. Dollars  Mil. of U.S. $  Not Seasonally Adjusted   \n",
       "1  Millions of U.S. Dollars  Mil. of U.S. $  Not Seasonally Adjusted   \n",
       "2  Millions of U.S. Dollars  Mil. of U.S. $  Not Seasonally Adjusted   \n",
       "\n",
       "  seasonal_adjustment_short              last_updated  \\\n",
       "0                       NSA 2024-08-15 15:37:49-05:00   \n",
       "1                       NSA 2024-08-15 15:37:36-05:00   \n",
       "2                       NSA 2024-08-15 15:37:40-05:00   \n",
       "\n",
       "                                               notes  popularity  \\\n",
       "0  Reverse repurchase agreements are transactions...          63   \n",
       "1  Reverse repurchase agreements are transactions...          40   \n",
       "2                                                NaN          29   \n",
       "\n",
       "   group_popularity  \n",
       "0                63  \n",
       "1                40  \n",
       "2                29  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding \"Reverse Repo\" to the search returns the second series in the equation, as the first result.\n",
    "\n",
    "obb.economy.fred_search(\"Wednesday Levels Reverse Repo\").to_df().head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>title</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WDTGAL</td>\n",
       "      <td>Liabilities and Capital: Liabilities: Deposits...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2024-08-14</td>\n",
       "      <td>Weekly, As of Wednesday</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2024-08-15 15:38:33-05:00</td>\n",
       "      <td>This account is the primary operational accoun...</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D2WLTGAL</td>\n",
       "      <td>Liabilities and Capital: Liabilities: Deposits...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2024-08-14</td>\n",
       "      <td>Weekly, As of Wednesday</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2024-08-15 15:38:37-05:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WLDLCL</td>\n",
       "      <td>Liabilities and Capital: Liabilities: Deposits...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2024-08-14</td>\n",
       "      <td>Weekly, As of Wednesday</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2024-08-15 15:36:57-05:00</td>\n",
       "      <td>This item is the sum of \"Term deposits held by...</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  series_id                                              title  \\\n",
       "0    WDTGAL  Liabilities and Capital: Liabilities: Deposits...   \n",
       "1  D2WLTGAL  Liabilities and Capital: Liabilities: Deposits...   \n",
       "2    WLDLCL  Liabilities and Capital: Liabilities: Deposits...   \n",
       "\n",
       "  observation_start observation_end                frequency frequency_short  \\\n",
       "0        2002-12-18      2024-08-14  Weekly, As of Wednesday               W   \n",
       "1        2002-12-18      2024-08-14  Weekly, As of Wednesday               W   \n",
       "2        2002-12-18      2024-08-14  Weekly, As of Wednesday               W   \n",
       "\n",
       "                      units     units_short      seasonal_adjustment  \\\n",
       "0  Millions of U.S. Dollars  Mil. of U.S. $  Not Seasonally Adjusted   \n",
       "1  Millions of U.S. Dollars  Mil. of U.S. $  Not Seasonally Adjusted   \n",
       "2  Millions of U.S. Dollars  Mil. of U.S. $  Not Seasonally Adjusted   \n",
       "\n",
       "  seasonal_adjustment_short              last_updated  \\\n",
       "0                       NSA 2024-08-15 15:38:33-05:00   \n",
       "1                       NSA 2024-08-15 15:38:37-05:00   \n",
       "2                       NSA 2024-08-15 15:36:57-05:00   \n",
       "\n",
       "                                               notes  popularity  \\\n",
       "0  This account is the primary operational accoun...          64   \n",
       "1                                                NaN          60   \n",
       "2  This item is the sum of \"Term deposits held by...          27   \n",
       "\n",
       "   group_popularity  \n",
       "0                64  \n",
       "1                60  \n",
       "2                27  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Refining the search for the Treasury General Account, returns the final series in the equation, as the first result.\n",
    "\n",
    "obb.economy.fred_search(\"Wednesday Levels Treasury General\").to_df().head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>last_updated</th>\n",
       "      <th>notes</th>\n",
       "      <th>popularity</th>\n",
       "      <th>group_popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SP500</td>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>2014-08-22</td>\n",
       "      <td>2024-08-21</td>\n",
       "      <td>Daily, Close</td>\n",
       "      <td>D</td>\n",
       "      <td>Index</td>\n",
       "      <td>Index</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2024-08-21 19:21:03-05:00</td>\n",
       "      <td>The observations for the S&amp;P 500 represent the...</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  series_id    title observation_start observation_end     frequency  \\\n",
       "0     SP500  S&P 500        2014-08-22      2024-08-21  Daily, Close   \n",
       "\n",
       "  frequency_short  units units_short      seasonal_adjustment  \\\n",
       "0               D  Index       Index  Not Seasonally Adjusted   \n",
       "\n",
       "  seasonal_adjustment_short              last_updated  \\\n",
       "0                       NSA 2024-08-21 19:21:03-05:00   \n",
       "\n",
       "                                               notes  popularity  \\\n",
       "0  The observations for the S&P 500 represent the...          83   \n",
       "\n",
       "   group_popularity  \n",
       "0                83  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Several major equity indices are published to FRED, S&P 500 is one of them.\n",
    "\n",
    "obb.economy.fred_search(\"SP500\").to_df().head(2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By looking at the descriptions, we can confirm that all three Federal Reserve series are numbers as `Millions of USD`.  If they were not all equivalent, some adjustments would need to be made before applying the equation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'WALCL: Millions of U.S. Dollars'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'WLRRAL: Millions of U.S. Dollars'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'WDTGAL: Millions of U.S. Dollars'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'SP500: Index'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for id in metadata:\n",
    "    display(f\"{id}: {metadata[id]['units']}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspecting the time series element shows that the S&P 500 data (as published to FRED) does not extend as far back as the others.  Let's drop the NaN values and start the time series at a common starting point, which is approximately ten years ago."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WALCL</th>\n",
       "      <th>WLRRAL</th>\n",
       "      <th>WDTGAL</th>\n",
       "      <th>SP500</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-12-18</th>\n",
       "      <td>719542.0</td>\n",
       "      <td>21905.0</td>\n",
       "      <td>6595.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-12-25</th>\n",
       "      <td>732059.0</td>\n",
       "      <td>20396.0</td>\n",
       "      <td>4662.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>730994.0</td>\n",
       "      <td>21091.0</td>\n",
       "      <td>4420.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-08</th>\n",
       "      <td>723762.0</td>\n",
       "      <td>18709.0</td>\n",
       "      <td>5490.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               WALCL   WLRRAL  WDTGAL  SP500\n",
       "date                                        \n",
       "2002-12-18  719542.0  21905.0  6595.0    NaN\n",
       "2002-12-25  732059.0  20396.0  4662.0    NaN\n",
       "2003-01-01  730994.0  21091.0  4420.0    NaN\n",
       "2003-01-08  723762.0  18709.0  5490.0    NaN"
      ]
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     "metadata": {},
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    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WALCL</th>\n",
       "      <th>WLRRAL</th>\n",
       "      <th>WDTGAL</th>\n",
       "      <th>SP500</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-08-27</th>\n",
       "      <td>4413736.0</td>\n",
       "      <td>282002.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-03</th>\n",
       "      <td>4415587.0</td>\n",
       "      <td>250306.0</td>\n",
       "      <td>21036.0</td>\n",
       "      <td>2000.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-10</th>\n",
       "      <td>4421408.0</td>\n",
       "      <td>267602.0</td>\n",
       "      <td>31872.0</td>\n",
       "      <td>1995.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-17</th>\n",
       "      <td>4449588.0</td>\n",
       "      <td>252224.0</td>\n",
       "      <td>123965.0</td>\n",
       "      <td>2001.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                WALCL    WLRRAL    WDTGAL    SP500\n",
       "date                                              \n",
       "2014-08-27  4413736.0  282002.0   29547.0  2000.12\n",
       "2014-09-03  4415587.0  250306.0   21036.0  2000.72\n",
       "2014-09-10  4421408.0  267602.0   31872.0  1995.69\n",
       "2014-09-17  4449588.0  252224.0  123965.0  2001.57"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data.to_df().head(4))\n",
    "display(data.to_df().dropna().head(4))\n",
    "\n",
    "# We'll create a new DataFrame object with the dropped rows.\n",
    "liquidity_index = DataFrame(data.to_df().dropna())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying the formula will simply be a matter of subtracting the first, three, columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WALCL</th>\n",
       "      <th>WLRRAL</th>\n",
       "      <th>WDTGAL</th>\n",
       "      <th>SP500</th>\n",
       "      <th>USD Liquidity Index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-07-24</th>\n",
       "      <td>7205455.0</td>\n",
       "      <td>805967.0</td>\n",
       "      <td>767419.0</td>\n",
       "      <td>5427.13</td>\n",
       "      <td>5632069.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-31</th>\n",
       "      <td>7178391.0</td>\n",
       "      <td>813261.0</td>\n",
       "      <td>854001.0</td>\n",
       "      <td>5522.30</td>\n",
       "      <td>5511129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-07</th>\n",
       "      <td>7175256.0</td>\n",
       "      <td>681881.0</td>\n",
       "      <td>785233.0</td>\n",
       "      <td>5199.50</td>\n",
       "      <td>5708142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-14</th>\n",
       "      <td>7177688.0</td>\n",
       "      <td>722198.0</td>\n",
       "      <td>788823.0</td>\n",
       "      <td>5455.21</td>\n",
       "      <td>5666667.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                WALCL    WLRRAL    WDTGAL    SP500  USD Liquidity Index\n",
       "date                                                                   \n",
       "2024-07-24  7205455.0  805967.0  767419.0  5427.13            5632069.0\n",
       "2024-07-31  7178391.0  813261.0  854001.0  5522.30            5511129.0\n",
       "2024-08-07  7175256.0  681881.0  785233.0  5199.50            5708142.0\n",
       "2024-08-14  7177688.0  722198.0  788823.0  5455.21            5666667.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liquidity_index[\"USD Liquidity Index\"] = (\n",
    "    liquidity_index[\"WALCL\"] - liquidity_index[\"WLRRAL\"] - liquidity_index[\"WDTGAL\"]\n",
    ")\n",
    "\n",
    "liquidity_index.tail(4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that there are two items to compare, let's draw it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "name": "USD Liquidity Index (Billions)",
         "type": "scatter",
         "x": [
          "2014-08-27",
          "2014-09-03",
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          "2014-09-17",
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         "x": 0.5,
         "y": 0.9
        },
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         "showgrid": false,
         "side": "left",
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         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_scatter(\n",
    "    x=liquidity_index.index,\n",
    "    y=liquidity_index[\"USD Liquidity Index\"] / 1000,\n",
    "    name=\"USD Liquidity Index (Billions)\",\n",
    "    yaxis=\"y1\",\n",
    ")\n",
    "\n",
    "fig.add_scatter(\n",
    "    x=liquidity_index.index,\n",
    "    y=liquidity_index[\"SP500\"],\n",
    "    name=\"S&P 500 Index\",\n",
    "    yaxis=\"y2\",\n",
    ")\n",
    "\n",
    "fig.update_layout(\n",
    "    yaxis=dict(\n",
    "        title=\"USD Liquidity Index (Billions)\",\n",
    "        side=\"left\",\n",
    "        position=0,\n",
    "        titlefont=dict(size=12),\n",
    "        showgrid=False,\n",
    "    ),\n",
    "    yaxis2=dict(\n",
    "        title=\"S&P 500 Index\",\n",
    "        side=\"right\",\n",
    "        overlaying=\"y\",\n",
    "        position=1,\n",
    "        titlefont=dict(size=12),\n",
    "    ),\n",
    "    title=\"USD Liquidity Index vs. S&P 500 Index\",\n",
    "    title_y=0.90,\n",
    "    title_x=0.5,\n",
    "    autosize=True,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To draw them both on the same y-axis, they will need to be normalized.  There are several methods for normalizing a series, the fourth function in the block below paramaterizes a few of them, making it easy to A/B them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USD Liquidity Index</th>\n",
       "      <th>SP500</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-07-31</th>\n",
       "      <td>0.643578</td>\n",
       "      <td>0.970490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-07</th>\n",
       "      <td>0.698835</td>\n",
       "      <td>0.885139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-14</th>\n",
       "      <td>0.687202</td>\n",
       "      <td>0.952750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            USD Liquidity Index     SP500\n",
       "date                                     \n",
       "2024-07-31             0.643578  0.970490\n",
       "2024-08-07             0.698835  0.885139\n",
       "2024-08-14             0.687202  0.952750"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_axis = liquidity_index[[\"USD Liquidity Index\", \"SP500\"]]\n",
    "\n",
    "\n",
    "def absolute_maximum_scale(series):\n",
    "    return series / series.abs().max()\n",
    "\n",
    "\n",
    "def min_max_scaling(series):\n",
    "    return (series - series.min()) / (series.max() - series.min())\n",
    "\n",
    "\n",
    "def z_score_standardization(series):\n",
    "    return (series - series.mean()) / series.std()\n",
    "\n",
    "\n",
    "methods = {\n",
    "    \"z\": z_score_standardization,\n",
    "    \"m\": min_max_scaling,\n",
    "    \"a\": absolute_maximum_scale,\n",
    "}\n",
    "\n",
    "\n",
    "def normalize(data: DataFrame, method: str = \"z\") -> DataFrame:\n",
    "    for col in data.columns:\n",
    "        data.loc[:, col] = methods[f\"{method}\"](data.loc[:, col])\n",
    "\n",
    "    return data\n",
    "\n",
    "\n",
    "normalized = normalize(y_axis, method=\"m\")\n",
    "\n",
    "normalized.tail(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now they can be easily plotted using the built-in `DataFrame.plot` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
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   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pd.options.plotting.backend = \"plotly\"\n",
    "normalized.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you just want to visualize the results, this is a fast way of doing it. However, titles and other customizatons clean things up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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      "application/vnd.plotly.v1+json": {
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        "plotlyServerURL": "https://plot.ly"
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        },
        "title": {
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         "x": 0.5,
         "y": 0.9
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       }
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     },
     "metadata": {},
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   ],
   "source": [
    "fig = go.Figure()\n",
    "\n",
    "fig.add_scatter(\n",
    "    x=normalized.index, y=normalized[\"USD Liquidity Index\"], name=\"USD Liquidity Index\"\n",
    ")\n",
    "\n",
    "fig.add_scatter(x=normalized.index, y=normalized[\"SP500\"], name=\"S&P 500 Index\")\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"USD Liquidity Index vs. S&P 500 Index (Normalized)\",\n",
    "    title_y=0.90,\n",
    "    title_x=0.5,\n",
    "    autosize=True,\n",
    ")"
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  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The combinations are endless and we love seeing your creations, tag us on social media with your custom indexes and indicators."
   ]
  }
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